So there are finally some signs if life in seed-stage technology funding:

In nominal terms, we roughly equaled the global peak from 2005.

Seed stage valuations have remained flat since 2011.

Adjusting for the size of the economy and our wealth, the level is still down.

Kind of hard to call this situation a “bubble”. But I can live with calling it a “recovery”.

Once again, see here, here, here, here, here, and here for previous posts in this thread. My data sources are the Center for Venture Research for angel data, the NVCA for VC data, and my personal tracking spreadsheet for “super angel” funds not part of the NVCA. For super angel investment, I worry most about detecting new chunks of money, not necessarily measuring the “true” level. I use the HALO report for pricing data, which goes back to 2011.

It looks like all the components are recovering, though traditional VC somewhat more slowly and super angel somewhat more quickly. The question is still, “Bubble or no bubble?”

Let’s look at prices, as I did in my 1H2013 post. According to the full year 2013 HALO report, the median seed valuation is still $2.5M… just like 2012… and 2011. The 75th percentile valuation is up slightly in 2013, from $3.7M to $4.2M. But the 25th percentile valuation is down a hair from $1.5M to $1.4M. According to the methodology described in the report, this data includes angel group deals before Series A. So what I think is happening is that some companies that might have gone for a VC round in the past are doing a larger angel round instead. If you check out my spreadsheet, you can see that check sizes for what the NVCA calls “seed” have taken another swing up, probably pushing some early startups out of that market. So no obvious pricing pressure.

Moreover, I think the following graphs make a bubble quite unlikely. I’ve been waiting for years to pull these out. The first one “deflates” the seed investment levels by adjusting for GDP. Thus it measures how seed investment has changed relative to total economic output. The second one deflates seed investment levels by adjusting for the level of the S&P500 index (on July 1 of the given year). Thus it measures how seed investment has changed relative to the total stock of wealth.

Compared to our economic output and total wealth, seed-stage investment seems like it still has a significant amount of headroom. I’m actually pretty sure I could build a darned accurate forecasting model based mostly on the S&P. Given that the index is up roughly 25% from July 2013 to July 2014, my eyeball estimate is that 1H2014’s numbers will show us somewhere around a $16B annual rate.

Well, it’s been almost three years since I started watching for quantitative evidence of a “bubble” in seed-stage technology funding. I feel like a broken record saying there’s still no sign. Here are the highlights:

1H2013 volume is 30% below the 2005 global maximum

1H2013 volume is 10% below the 2011 local maximum

Seed stage valuations have been flat since 2011

You simply don’t have a bubble when volume is down and prices are flat!

To review the history of my seed bubble watch, see here, here, here, here, and here. Recall that I use the Center for Venture Research’s angel data, the NVCA’s VC data, and my personal list of “super angel” funds not part of the NVCA. The volume calculation methodology is not designed to produce the most accurate estimate of the true number of seed-stage dollars. Rather, I want it maximally sensitive to sudden influxes in new seed money. I use the HALO report for pricing data, which started coming out in 2011.

The story continues to be that traditional VCs have become increasingly irrelevant as their seed dollars have dropped 60% from 2009 to 1H2013 and their share of all seed dollars has plunged from 22% to 7.5%.

Angel’s position has gradually eroded from 2011 to 2013, with their share decreasing from 88% to 77%. Super angels and seed funds have gained in share during that time, jumping from 3.0% to 15%. My guess is that trend will continue unless the individual angel pool increases via new platforms like AngelList. In any case, the new breed of funds is not growing fast enough yet to make up for decreases from other sources.

[Edit 8pm: Somehow this paragraph got deleted from my draft.] There also appears to be no pricing pressure at the seed stage. According to the 2012 and 2Q2013 HALO reports, the median seed-stage pre-money valuation has remained $2.5M since 2011. Moreover, the 25th and 75th percentile valuations have actually decreased, making it hard to argue that there is some hidden dynamic masking a buildup in prices.

Interestingly, the HALO report shows a continued drop in California’s share of angel group activity. From 21.0% in 2011, to 18.1% in 2012, to 17.3% in 1H2013. I’ll take this as continued confirmation that RSCM is right that some of the best values are outside the Bay Area.

It will be interesting to see what the data shows for 2H2013 and 1H2014. With the S&P reaching new highs throughout 4Q2013, institutions should increase their allocations to alternative investment funds and angels should feel like they have more wealth to invest in startups. Assuming the public markets don’t experience a sudden drop in the beginning of 2014, of course.

While my goal is to eventually apply the Market Space model to large enterprises, I’m going to begin with startups. Obviously, my work at RSCM makes startup close to my heart. And most large enterprises were new entrants at some point, so analyzing the birth of firms seems like it should lay some crucial groundwork. (For previous posts in this series, see here: one, two, three, four.)

Looking at the search for profitable products as a Multi-Armed Bandit (MAB) problem illuminates the general complexity of the firm’s challenge (see previous posts in this series: one, two, three). But in terms of analyzing specific firm behaviors, I think it’s important to acknowledge that we don’t have a pure MAB here. It seems pretty clear there’s more causal structure in Market Space.

In the last post, I presented my 30,000-foot view of Market Space. I think it already provides some intuition. From the final diagram, you can literally see the firm’s search problem. But our eventual goal is a more formal model. So we need to drop our altitude a bit.

In our last episode, I sketched out the goals for my new model of the firm. In this post, I’ll present the high level view of my model, which I call “Market Space” (yes, we’ll be using a high-dimensional space again).

As I’ve written before, I am not entirely satisified with how microeconomic theory characterizes firms. So I’m going to take another tilt at the windmill and try to develop a model more suited to answering the questions I find puzzling. Note, the body of posts in this series will appear below the fold. They are as much for my own benefit as readers’, so probably interesting to a limited audience. And to any professional economists who may read this series, please be gentle. I realize it’s a rather presumptuous undertaking. But I’m more trying to work through my own thoughts rather than attempting to advance the field.